Hugging Face's logo

Datasets:
tasal9
/
ZamAI-Pashto-Dataset-Cleaned

Tasks:
Text Classification
Text Generation
Question Answering
Languages:
Pashto
Size:
10K<n<100K
License:
Dataset card Files
xet
Community
ZamAI-Pashto-Dataset-Cleaned / README.md
tasal9's picture
tasal9
Enhance README.md with detailed dataset information and usage guidelines
6e5195c
preview code
|
raw
history blame contribute delete
3.02 kB
metadata
license: apache-2.0
language:
  - ps
size_categories:
  - 10K<n<100K
task_categories:
  - text-classification
  - text-generation
  - question-answering

ZamAI Pashto Dataset (Cleaned)

Dataset Summary

This repository hosts a cleaned and QC'ed slice of the ZamAI Pashto corpus. The release contains 28,650 Pashto-language articles and prompts that have been deduplicated, normalised, and aligned for instruction-style training as well as prompt/completion workflows.

Dataset Details

  • Curated by: ZamAI Team
  • Language(s): Pashto (ps)
  • License: Apache-2.0
  • Version: v1.0 (2025-06-23)
  • Sources: BBC Pashto, Radio Azadi, community-contributed Pashto corpora
  • Processing Pipeline: ZamAI Pashto Data Processing Pipeline

Dataset Structure

  • CSV splits: pashto_cleaned_train.csv, pashto_cleaned_val.csv, pashto_cleaned_full_dataset.csv
  • Instruction JSONL: pashto_train_instruction.jsonl, pashto_val_instruction.jsonl
  • Prompt/Completion JSONL: pashto_train_prompt_completion.jsonl, pashto_val_prompt_completion.jsonl

Fields

Field Description
title Source headline or generated title
text Cleaned Pashto article body
source Origin of the example (news outlet / pipeline tag)
prompt Instruction-style prompt derived from the article
completion Expected model output/completion
instruction (JSONL) Instruction text for instruction-tuning
input (JSONL) Optional input/context paired with the instruction
output (JSONL) Target response

Splits

  • train: 25,785 examples
  • validation: 2,865 examples
  • full: 28,650 examples (union of train + validation)

Accessing the Data

Files are tracked with Git LFS. After cloning, run git lfs pull in the repository to download the actual CSV/JSONL payloads.

Cleaning & Normalisation

  1. Dropped rows with empty title/text
  2. Removed duplicate content hashes
  3. Normalised whitespace and Unicode (NFKC)
  4. Filtered samples shorter than 10 characters
  5. Generated aligned prompts, completions, and instruction templates

Intended Uses

  • Fine-tuning Pashto T5/mT5 style models
  • Instruction-tuning chat assistants for Pashto
  • Building evaluation sets for Pashto summarisation and QA

Limitations

  • Dominated by news-domain writing; colloquial data is limited
  • Automatically generated prompts/completions may include occasional artefacts—consider manual review before deployment
  • Despite cleaning, residual duplicated facts may remain due to mirrored reporting across sources

Citation 

@misc{tasal2025_zamai_pashto_cleaned,
  title = {ZamAI Pashto Dataset (Cleaned)},
  author = {Yaqoob Tasal and the ZamAI Team},
  year = {2025},
  howpublished = {\url{https://huggingface.co/datasets/tasal9/ZamAI-Pashto-Dataset-Cleaned}}
}